The so called intelligent system in the modern artificial intelligence technology includes at least two necessary factors: one is the knowledge representation model; another is the inference method based on this knowledge representation model. The two factors are necessary for constructing an intelligent system, because the inference method is directly based on the knowledge representation model. Different knowledge representation model decides different inference method. Vice versa, choosing which knowledge representation model must consider not only the capability of the representation, the flexibility of using this representation model, and the difficulty of obtaining or learning the related data, but also how to build the reasonable and effective inference method based on the knowledge representation model. Such inference method must satisfy the natural law (in this invention, it is the basic law of probability theory), and have the capability and efficiency of dealing with various problems in real applications. In other words, the knowledge representation model and the inference method is an organic whole body. They together compose an intelligent system. Therefore, to construct an intelligent system must include a knowledge representation model and an inference method in accordance with this knowledge representation model.
According to the knowledge representation model and the inference method included in the intelligent system, the ordinary software engineers can develop the specific intelligent system software products by using various software development tools. By installing such a software product in a computer, this computer becomes a specific intelligent system device. As users, the domain engineers can input the data or information related to their problems to be solved by means of the functions provided by this device. In cope with the obtained information online, this device can perform the specific inference computation and provide the useful information to help solve the problems. If this intelligent system device including the knowledge of the specific application is installed as a component in the control system with closed loop, the intelligent automatic control can be realized.
The engineers in different domains may input different knowledge/information by means of the same functions provided by the same intelligent system device and use it in different domains. Therefore, the intelligent system can usually be used in many areas and have great commercial values.
Uncertain causality information is a particularly important type of information of various types of knowledge information to be dealt with by intelligent systems. This makes the research and development of the intelligent systems dealing with the uncertain causality information be an important development direction of the intelligent system technology, because such intelligent systems can be widely applied in the fault diagnosis of industrial systems, the prediction of disasters, the analyses of economy or finance, the risk prediction, the detection, the decision consultation, etc. So far, a lot of resources have been invested in this area by many countries in the world. For example, the relative program in the National Natural Science Foundation of China is named as “intelligent system and knowledge engineering”.
The online fault diagnosis of nuclear power plants is one of the examples of applying such intelligent systems dealing with the uncertain causality information. The main parameters and the component states related to the plant operation are collected in the control room through the data collection system, and are displayed in various instrument meters. The task of the operators is to check these data periodically, judge whether or not they are normal; when abnormal case or alarm appears, diagnose the root cause and take measures in time, so as to remove or control the fault. Usually, however, the number of important parameters is in the scale of a few hundreds, the amount of data is huge, the situations are complex, and the burden of the operators in the control room is heavy. These factors cause the nervous moods of the operators, leading to the difficulty of correctly diagnosing the root cause of the abnormal state and of taking the correct measures in time. This may result in a big loss.
The Three Mile Island accident of the US in 1979 is such a typical example. This accident is caused by an ordinary component failure. But the operators made an incorrect judgment to the abnormal signals and took an incorrect measure. Not only the fault was not removed or controlled, but the fault was enlarged, resulting in a serious accident, while the root cause is just a small failure. The core of the reactor was burned. The whole nuclear power plant was ruined down.
The astounding Chernobyl nuclear power plant accident happened during the post Soviet Union is also caused by the incorrect judgment of operators and incorrect measures. It causes a lot of death, wounded persons and a big loss of property. So far, the serious result has not disappeared yet.
The prediction to the flood is also an important engineering and technical problem related to intelligent systems. This problem usually deals with the comprehensive analysis and prediction to the possibility of the dangerous down river water level in the following days, the judgment to the degree of danger, and then providing the gist for decisions such as remove people, reinforce the bank, or even bomb the bank somewhere for flood discharge, etc., so as to reduce the loss of flood. It is a realistic technical scheme to apply the intelligent system to solve this problem, based on which the uncertain inference prediction can be made according to the uncertain parameters such as the water levels at different places of the valley, the weather forecast, etc., and the uncertain causalities among them.
The traditional method to deal with the causality information is the rule-based expert system. This methodology takes the rules in the type of IF-THEN to represent the causalities among the real things. For example, the Chinese patent (ZL90103328.6) named as “computer aided decision method” by TIC of the US is such a rule-based expert system. The rule-based expert systems are good in the cases without uncertainty. Its technical scheme involves mainly how to represent and organize the rules and facts, as well as how to invoke, match and eliminate these rules and facts in the inference. When uncertainties exist, the technologies dealing with uncertainties have to be applied.
The uncertainty (including dynamical cases) is currently the important research area in this field. This is because the uncertainty exists universally and is the most difficult technical problem. For this, the international academic community establishes the association of uncertainty in artificial intelligence (AUAI) and holds international conferences every year. So far, such conferences have been held more than 20 times (www.auai.org).
The certainty factor method presented by Shortliffe, the evidence reasoning method presented by Dempster-Shafer, the fuzzy logic method presented by Zahdy, etc., take the non-probabilistic parameters to measure the uncertainty. Although they have unique features, their applications are limited due to the limitations of the non-probabilistic parameters themselves and other causes.
Of the intelligent systems dealing with uncertainty, the intelligent systems that take graphs instead of only languages in dealing with the uncertain causality information are more and more welcome by users. This is because the graphs are intuitive to be understood, convenient in representation, etc., in which the neural network (NN) was and is still one of such methods. For example, the recent granted Chinese invention patents No. 01139043.3 named as “the structure based method for the construction and optimization of NN”, No. 03137640.1 named as “the NN based processing method for information pattern recognition” and No. 02139414.8 named as “the recognition method for the chaos signals and general noise”, etc., are such methods. NN imitates brain's neural network, adjusts the network structure and parameters by learning from a large amount of data, so as to obtain and represent the knowledge. After this, the states of the things in concern can be inferred according to the observed information. However, due to the lack of the enough research to the neural network of brain and the limitations of NN such as the black or gray box representation model that does not correspondingly and clearly reflect the logics among the things in concern, and the lack of the data to learn from, its applications mainly focused on the pattern recognition and some other areas. There are less applications dealing with uncertain causality information.
The Bayesian/Bayes/Belief Network (BN) presented by Judea Pearl, al. has been so far another good method to deal with the uncertain causality problems. Its feature is to use the direct acyclic graph (DAG) to represent the causalities among the things and use the conditional probability table (CPT) to represent the degree of the causality uncertainty. Then, based an the observed evidence E, the forward, backward or bidirectional probabilistic inference can be made. FIG. 40 shows two examples of BN, in which FIG. 40-1 is singly connected and FIG. 40-2 is multiply connected. The directed arcs represent the causalities. The static logic cycles (e.g. FIG. 41) are prohibited in BN, because otherwise, there is no solution. In general, BN is graphically intuitive, has clear physical meaning, based on the probability theory strictly, easy to utilize the statistical data, localized in the computation steps, self-consistent as a whole theory framework, etc. But, although its every computation step is localized, the probability distributions of the nodes in concern can only be obtained by the computation of bidirectional information propagation throughout the whole network. This increases the computation amount significantly. Moreover, it is difficult for BN to deal with the static logic cycles, the dynamical change of data and logic structures, and the lack, imprecision and incompleteness of data, etc. In spite of this, BN is still applied widely.
Because of the success of BN, Judea Pearl won the excellent research award issued once per two years by IJCAI in 1999, as well as many other awards (http://bayes.cs.ucla.edu/). So far, BN has become one of the popular intelligent systems.
Based on the advantages of BN such as the graphical representation and strictly based on probability theory, etc., a method named as the single-valued Dynamical Causality Diagram (DCD) was developed in 1994. FIG. 42 is an example of the single-valued DCD. This single-valued DCD includes AND gate, OR gate and the logical cycle. But every variable can only has one state for which the causalities can be represented, and another state for which no causality can be represented. Otherwise, the case becomes multi-valued. The DCD solves, in the single-valued cases, the problems of logic cycles, dynamics, representing the causalities with linkage intensities instead of CPTs so that the statistic dada can be less relied on, etc. The single-valued DCD method involves the disjoint operation of logic expressions, which is an NP hard difficulty. That is to say, as the scale of cases becomes large, there will be the combination explosion in the disjoint operation.
In 2001, a further method (briefly called the multi-valued DCD) was developed, which transfers the multi-valued cases into the single-valued cases. This method treats all the abnormal states of a variable as a polymeric state besides a normal state whose causalities are not represented, so as to transfer the multi-valued cases as the single-valued cases for computation. After this, the probability of the polymeric state is allocated among the abnormal states according to some proportion. However, the theory of computing the proportion is not well founded. It does not really solve the conflict between the independence of representing knowledge and the correlation resulted from the exclusion among the different states of a variable in a multi-valued DCD. Furthermore, it requires that every variable has a special normal state for which no causality should be represented. Therefore, it is not sound and cannot be widely applied. Moreover, the free mixture and transformation from each other between the explicit representation of the multi-valued DCD and the implicit CPT representation of BN, the fuzzy evidence, the free mixture and unified treatment of the discrete and continuous variables, the complex logic combinations, the lack or incompleteness of data, the dynamics, etc., have not been solved yet.